Unsupervised Methods for Intrusion Detection Systems and Forensic Examination
Lisa Gopal1, Samir Rana2, Preeti Chaudhary3, Vrince Vimal4
1Lisa Gopal, Assistant Professor, Graphic Era Hill University, (Dehradun), India.
2Samir Rana, Assistant Professor, Graphic Era Hill University, (Dehradun), India.
3Preeti Chaudhary, Assistant Professor, Graphic Era Hill University, (Dehradun), India.
4Vrince Vimal, Associate Professor, Graphic Era Hill University, (Dehradun), India.
Manuscript received on 27 April 2019 | Revised Manuscript received on 09 May 2019 | Manuscript Published on 18 May 2019 | PP: 32-35 | Volume-8 Issue-6S4 November 2019 | Retrieval Number: F10041186S419/19©BEIESP | DOI: 10.35940/ijeat.F1004.1186S419
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Crime is increasing with the widespread growth of digital world. The last decade has witnessed the elevation in the diversity and frequency of malicious usage of the network. Forensic investigators play a paramount role in the investigation based upon collection and analysis of facts from the crime scene. Intrusion Detection Systems, which are in use till date do not enlighten the trends in attack as they are built on various outmoded attack classes. IDSs that uses unsupervised techniques has been discussed in the literature. It is based on the requirement of labelled data as it is required in regular training or on the characteristics that elaborates each class without any knowledge in the prior. Despite of being widely popular among researchers and mammoth practical applications, fidelity of IDS Is yet debatable. This paper provides an exhaustive survey of the various unsupervised anomaly-based intrusion detection techniques and their potential usage in their respectivedomain.
Keywords: Forensic, IDS, Unsupervised Methods, Attacks.
Scope of the Article: Probabilistic Models and Methods